Deep Learning Chest X-Ray Age, Epigenetic Aging Clocks and Associations with Age-Related Subclinical Disease in the Project Baseline Health Study.
Authors
Affiliations (12)
Affiliations (12)
- Cardiovascular Imaging Research Center (CIRC), Department of Radiology, Massachusetts General Hospital & Harvard Medical School, Boston, MA, USA.
- Harvard Medical School, Harvard University, Boston, MA, USA.
- Verily Life Sciences LLC, San Francisco, CA.
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Medicine, Duke University School of Medicine, Durham, NC, USA.
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA.
- Stanford Center for Clinical Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
- Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, USA.
- Hinda and Arthur Marcus Institute for Aging Research, Boston, MA, USA.
- Department of Medicine, Beth Israel Deaconess Medical Center & Harvard Medical School, Boston, MA, USA.
- Program for Artificial Intelligence in Medicine (AIM), Brigham and Women's Hospital & Harvard Medical School, Boston, MA, USA.
Abstract
Chronological age is an important component of medical risk scores and decision-making. However, there is considerable variability in how individuals age. We recently published an open-source deep learning model to assess biological age from chest radiographs (CXR-Age), which predicts all-cause and cardiovascular mortality better than chronological age. Here, we compare CXR-Age to two established epigenetic aging clocks (First generation-Horvath Age; Second generation-DNAm PhenoAge) to test which is more strongly associated with cardiopulmonary disease and frailty. Our cohort consisted of 2,097 participants from the Project Baseline Health Study, a prospective cohort study of individuals from four US sites. We compared the association between the different aging clocks and measures of cardiopulmonary disease, frailty, and protein abundance collected at the participant's first annual visit using linear regression models adjusted for common confounders. We found that CXR-Age was associated with coronary calcium, cardiovascular risk factors, worsening pulmonary function, increased frailty, and abundance in plasma of two proteins implicated in neuroinflammation and aging. Associations with DNAm PhenoAge were weaker for pulmonary function and all metrics in middle-age adults. We identified thirteen proteins that were associated with DNAm PhenoAge, one (CDH13) of which was also associated with CXR-Age. No associations were found with Horvath Age. These results suggest that CXR-Age may serve as a better metric of cardiopulmonary aging than epigenetic aging clocks, especially in midlife adults.